CN117877736B - Intelligent ring abnormal health data early warning method based on machine learning - Google Patents
Intelligent ring abnormal health data early warning method based on machine learning Download PDFInfo
- Publication number
- CN117877736B CN117877736B CN202410275598.9A CN202410275598A CN117877736B CN 117877736 B CN117877736 B CN 117877736B CN 202410275598 A CN202410275598 A CN 202410275598A CN 117877736 B CN117877736 B CN 117877736B
- Authority
- CN
- China
- Prior art keywords
- feature
- cluster
- index
- abnormality
- characteristic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000036541 health Effects 0.000 title claims abstract description 163
- 230000002159 abnormal effect Effects 0.000 title claims abstract description 126
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000010801 machine learning Methods 0.000 title claims abstract description 15
- 230000005856 abnormality Effects 0.000 claims abstract description 140
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 29
- 238000012937 correction Methods 0.000 claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 230000000739 chaotic effect Effects 0.000 claims description 25
- 238000010276 construction Methods 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 5
- 238000012544 monitoring process Methods 0.000 abstract description 8
- 238000004458 analytical method Methods 0.000 description 5
- 238000012216 screening Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 3
- 235000014435 Mentha Nutrition 0.000 description 2
- 241001072983 Mentha Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000006806 disease prevention Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 235000014569 mints Nutrition 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000008280 blood Substances 0.000 description 1
- 210000004369 blood Anatomy 0.000 description 1
- 210000001124 body fluid Anatomy 0.000 description 1
- 239000010839 body fluid Substances 0.000 description 1
- 230000001684 chronic effect Effects 0.000 description 1
- 238000011982 device technology Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000036578 sleeping time Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention relates to the technical field of abnormal data early warning, in particular to an intelligent ring abnormal health data early warning method based on machine learning, which comprises the following steps: collecting user health data; constructing an overall correlation between each feature and the user health abnormality index according to the difference between the user health abnormality indexes and the features; constructing the correlation between any two features according to the correlation between each feature and the user health abnormality index and the difference of the feature values; constructing a feature correction factor of each feature according to the correlation between any two features and the correlation between each feature and the user health abnormality index; and acquiring key characteristic items according to the overall correlation, the characteristic importance and the characteristic correction factors, and evaluating the health abnormal condition of the user by adopting an abnormality detection algorithm. The invention improves the accuracy of identifying key characteristic items in the user health data and improves the monitoring accuracy of the user health data.
Description
Technical Field
The application relates to the technical field of abnormal data early warning, in particular to an intelligent ring abnormal health data early warning method based on machine learning.
Background
With increasing attention to quality of life and health, there is an increasing need for convenient and accurate health management solutions. In particular, in chronic management, disease prevention, and promotion of health literacy, real-time monitoring and early intervention are becoming increasingly important. The preventive medicine concept emphasizes that disease prevention is better than treatment, health hidden dangers can be found in advance by utilizing intelligent rings and other devices, serious diseases can be effectively reduced, the pressure of a medical system is reduced, and social resources are saved. In recent years, wearable device technology has advanced rapidly, and intelligent finger rings are small and powerful wearable devices, and built-in miniature sensors of the intelligent finger rings can collect physiological data of human bodies in real time and continuously, so that a hardware foundation is provided for health monitoring.
When the isolated forest algorithm is used for carrying out anomaly detection on multidimensional data, as a dimension is randomly selected from each constructed data space, a large amount of dimension information is still unused after tree construction is completed, so that the reliability of the algorithm is reduced. However, a large number of noise dimensions or irrelevant dimensions may exist in the high-dimensional space, which affects the tree construction, and may further cause that the finally obtained abnormal detection result is not reliable.
Disclosure of Invention
In order to solve the technical problems, the invention provides an intelligent ring abnormal health data early warning method based on machine learning, so as to solve the existing problems.
The intelligent ring abnormal health data early warning method based on machine learning adopts the following technical scheme:
The embodiment of the invention provides an intelligent ring abnormal health data early warning method based on machine learning, which comprises the following steps:
Collecting user health data, including various characteristic values and artificially marked user health abnormality indexes;
Acquiring each characteristic cluster of each characteristic in each abnormal index cluster according to each user health abnormal index and the characteristic value; constructing a chaotic factor of each feature in the abnormal index cluster according to the feature cluster of each feature in the abnormal index cluster; constructing the correlation between each feature in the abnormality index cluster and the user health abnormality index according to the data difference between each feature and the integral feature in the abnormality index cluster and the chaotic factor; acquiring the overall correlation between each feature and the user health abnormality index according to the correlation between each feature and the user health abnormality index in different abnormality index cluster;
constructing the correlation between any two features in the abnormal index cluster according to the correlation between each feature in the abnormal index cluster and the user health abnormal index and the difference of the feature values; constructing the feature importance of each feature in the abnormal index cluster according to the correlation between any two features in the abnormal index cluster and the correlation between each feature and the user health abnormal index; constructing a feature correction factor of each feature based on the trend distribution similarity of feature importance among the features; constructing the significance of each feature according to the overall correlation between each feature and the user health abnormality index, the feature importance and the feature correction factor;
And acquiring key feature items based on the significance, and evaluating the health abnormal condition of the user by adopting an abnormality detection algorithm.
Preferably, the obtaining each feature cluster of each feature in each abnormality index cluster according to each user health abnormality index and the feature value includes:
obtaining clustering clusters of the user health abnormality indexes of all users by adopting a clustering algorithm, and marking the clustering clusters as the clustering clusters of the abnormality indexes;
Clustering the characteristic values of each characteristic in each abnormal index cluster in all users to obtain each cluster, and marking the cluster as each characteristic cluster of each characteristic in each abnormal index cluster.
Preferably, the constructing a chaotic factor of each feature in the abnormality index cluster according to the feature cluster of each feature in the abnormality index cluster includes:
for each characteristic cluster of each characteristic in the abnormal index cluster, acquiring the absolute value of the difference value of the element number between any two characteristic clusters and the absolute value of the difference value of the average characteristic value of all elements;
Acquiring the number of feature clusters of each feature in the abnormality index cluster; and calculating the sum of products of absolute values of differences of element numbers and absolute values of differences of average feature values of all elements between any two feature clusters in each feature in the abnormal index cluster, and taking the products of the sum and the number of the feature clusters as a chaotic factor of each feature in the abnormal index cluster.
Preferably, the constructing the correlation between each feature in the abnormality index cluster and the user health abnormality index according to the data difference between each feature and the whole feature in the abnormality index cluster and the chaotic factor includes:
For each user in the abnormal index cluster, calculating the ratio of the characteristic value of each user in each characteristic to the user health abnormal index of each user; calculating the ratio of the average characteristic value of all the users in each characteristic to the average value of the user health abnormality indexes of all the users;
Obtaining a chaotic factor of each feature in the abnormal index cluster; multiplying the sum of absolute values of differences between two ratios of all users in the abnormality index cluster by the chaotic factor to obtain the correlation between each feature in the abnormality index cluster and the health abnormality index of the user.
Preferably, the obtaining the overall correlation between each feature and the user health abnormality index according to the correlation between each feature and the user health abnormality index in the cluster of different abnormality indexes includes:
And calculating a normalized value of a sum value of the correlations of all the abnormal index clusters between each feature and the user health abnormal index, and taking the difference value between the number 1 and the normalized value as the integral correlation between each feature and the user health abnormal index.
Preferably, the correlation between any two features in the abnormal index cluster is constructed according to the correlation between each feature in the abnormal index cluster and the user health abnormal index and the difference of the feature values, and the expression is:
wherein, Representing the correlation between features o and s within an anomaly index cluster i, exp () represents an exponential function based on a natural constant,/>、/>Respectively representing the correlation between the characteristics o and s in the abnormal index cluster i and the user health abnormal index,/>Representing the number of elements within the anomaly index cluster i,/>、Characteristic values of characteristic o and characteristic s of the jth user in the abnormal index cluster i are respectively represented by/>、/>And respectively representing the average characteristic values of the characteristics o and the characteristics s of all the users in the abnormal index cluster i.
Preferably, the constructing the feature importance of each feature in the abnormality index cluster according to the correlation between any two features in the abnormality index cluster and the correlation between each feature and the user health abnormality index includes:
For each feature in the abnormality index cluster, calculating a sum of correlations between each feature in the abnormality index cluster and all the remaining features; and taking the ratio of the sum value to the correlation between each feature in the abnormality index cluster and the user health abnormality index as the feature importance of each feature in the abnormality index cluster.
Preferably, the building the feature correction factor of each feature based on the trend distribution similarity of feature importance among features includes:
Forming a feature graph for each feature based on the feature importance of each feature within the respective anomaly index clusters;
calculating the sum value of the similarity between the curves of each feature and the feature graphs of all other features after fitting, and taking the difference value between the number 1 and the normalized value of the sum value as the feature correction factor of each feature; the similarity is calculated by a shape context algorithm.
Preferably, the constructing the significance of each feature according to the overall correlation between each feature and the user health abnormality index, the feature importance and the feature correction factor includes:
Calculating the sum of the feature correction factor and the number 1 of each feature; acquiring feature importance average values of each feature in all abnormal index cluster; and taking the product of the sum value, the feature importance mean value and the overall correlation between each feature and the user health abnormality index as the significance of each feature.
Preferably, the obtaining the key feature item based on the significance, and evaluating the health abnormality of the user by using an abnormality detection algorithm includes:
The saliency of all the features is used as input of an Ojin threshold method, an Ojin threshold is obtained, and the features corresponding to the saliency larger than the Ojin threshold are used as key feature items;
The feature data of all key feature items of each user are subjected to an isolated forest algorithm, and the abnormal score of each user is obtained; and carrying out health early warning on the users with the anomaly scores larger than the preset threshold value.
The invention has at least the following beneficial effects:
According to the invention, through analyzing the acquired multidimensional data, the chaotic index of the characteristic item in the same abnormal index cluster where the user health abnormal index is located is calculated, and the correlation between the characteristic and the user health abnormal index is reflected based on the chaotic index, so that the chaotic index is used for evaluating the health regularity relation between the characteristic item and the user health data; meanwhile, the correlation among different features in the same user health anomaly cluster is calculated, and then the feature importance of the features is obtained by combining the correlation between the feature data and the user health anomaly indexes, and the importance degree of the features is analyzed from the whole and internal angles, so that the risk that important feature information is ignored is avoided, and the analysis is more comprehensive;
Further, according to trend conditions that the characteristics of different characteristic items change along with abnormal indexes of the health of the user, the trend similarity of any two characteristics is mined, and characteristic correction factors of the characteristics are constructed, so that the saliency of the characteristics is finally obtained, key characteristic items are extracted according to the saliency of characteristic data, and then the data of each key characteristic item are used for representing the characteristic degree of the health of the user, so that screening is carried out according to the obtained characteristic values, and the number of the characteristic data is reduced; and obtaining the abnormal score of the user health by using an isolated forest algorithm, and completing the monitoring of the abnormal user health. According to the invention, the key characteristic items are selected by reducing the dimensionality of the acquired data, so that the reliability of the abnormal value obtained by the isolated forest algorithm is greatly improved, and the accuracy of monitoring the health data of the user is greatly improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of the intelligent ring abnormal health data early warning method based on machine learning provided by the invention;
FIG. 2 is a flow chart of index construction of user health anomaly scores.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects of the intelligent ring abnormal health data early warning method based on machine learning according to the invention by combining the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the intelligent ring abnormal health data early warning method based on machine learning provided by the invention with reference to the accompanying drawings.
The embodiment of the invention provides a machine learning-based intelligent ring abnormal health data early warning method.
Specifically, the following intelligent ring abnormal health data early warning method based on machine learning is provided, please refer to fig. 1, the method comprises the following steps:
And S001, collecting user health data.
The intelligent ring is used for collecting user health data information, wherein the user health data information comprises various characteristic values such as heart rate information of a user, temperature and humidity information of the surface of skin of the user, blood oxygen saturation, number of steps of user activity, distance, activity time and consumed calories, sleeping time length, depth and quality of the user. And (3) manually marking the user health abnormality indexes of the obtained user health data of each user by using a manual marking method, wherein the value of the user health abnormality index F is [0,1], namely the greater the value is, the more abnormal the health condition of the user is indicated.
And (3) marking the obtained health data information of each user as a vector R= [ B, F, t ], wherein B represents user health data acquired by using the intelligent ring, F represents a user health abnormality index marked artificially, and t represents the time for acquiring the data by the intelligent ring.
So far, the user health data acquisition can be completed through the method.
Step S002, the degree of confusion of the features in the cluster is built based on the classification of the user health abnormality indexes, and the feature importance is analyzed based on the correlation between the features and the user health abnormality indexes and the correlation between the data features.
The characteristic screening is carried out on each characteristic information of the user, so that the characteristics relatively related to the health of the user are obtained, and the abnormal detection is carried out on the characteristics by using an isolated forest algorithm, so that the abnormal degree of the health of the user is obtained.
Because the basic information of different users and the data information collected by the intelligent ring in real time are more, the characteristic data with low correlation with the user health abnormality assessment index may exist, and when the random forest algorithm is used for assessing the user data with the health abnormality index, the situation that the isolated tree constructed by the characteristic data with low correlation is too many is possibly caused, and the result of the user health abnormality index is finally inaccurate is caused, the user characteristics are extracted by the method based on the user test data and the artificial labeling of the user health abnormality index, and the occurrence of the problems is further avoided.
According to the embodiment, through analyzing the feature data acquired by the intelligent ring of the user, the correlation between the feature data and the user health abnormality index and the correlation between the features are determined during the user data acquisition, and the feature degree of the feature representing the user health of the feature data is analyzed, so that screening is performed according to the feature degree of the obtained feature value, and the number of the feature data is reduced.
According to the embodiment, firstly, clustering analysis is carried out on manually marked user health abnormality indexes corresponding to user health data obtained through each acquisition, and the correlation between each item of characteristic data and the user health abnormality index is determined through analyzing the difference between each item of characteristic data collected in real time by corresponding users in a cluster where each similar user health index is located, wherein a DBSCAN (direct base sequence of event) clustering algorithm is used for a clustering algorithm, clustering parameters r=5, mints=5, the clustering distance is the absolute value of the difference value between the user health abnormality indexes, the number of the obtained clusters is recorded as K, and the obtained clusters are recorded as abnormal index clusters. The DBSCAN clustering algorithm is a well-known technique, and this embodiment is not described in detail.
According to the embodiment, through processing the characteristic values of all characteristic data in the cluster, the chaotic indexes of the characteristic items in the same abnormal index cluster where the user health abnormal indexes are located are analyzed, and the correlation between the characteristic and the user health abnormal indexes is evaluated and reflected according to the obtained chaotic indexes.
The method for the chaotic indexes of the data features in the abnormal index cluster comprises the following steps: clustering the characteristic values of the characteristic o in the abnormal index cluster, wherein the clustering algorithm also uses a DBSCAN clustering algorithm, the clustering parameter r=5, the mints=5, the clustering distance is the characteristic value of the characteristic o, the number of the obtained clusters is marked as H, and the cluster is marked as the characteristic cluster. Taking an abnormality index cluster i as an example, the method for calculating the chaotic degree of the feature o in the abnormality index cluster i is as follows:
wherein, A chaotic factor representing a feature o within an abnormality index cluster i, H representing the number of feature clusters in the abnormality index cluster i,/>Absolute value of difference value of number of cluster elements representing characteristic cluster u and v in abnormality index cluster i,/>, andThe absolute value of the difference of the average eigenvalues of the intra-cluster element eigenvalues of the eigenvector clusters u and v in the abnormality index cluster i is represented.
It should be noted that, when the number of feature clusters obtained by clustering according to the feature value of the feature o in the obtained abnormality index cluster i is larger, the difference of the number of elements in each feature cluster is larger, and the difference of the average values of the feature values of the elements in each feature cluster is also larger, the disorder factor of the feature o in the current abnormality index cluster i is larger.
Wherein,Representing the correlation between the feature o and the user health abnormality index within the abnormality index cluster i,Disorder factor representing feature o within abnormality index cluster i,/>Representing the number of elements within the anomaly index cluster i; /(I)A feature value representing a feature o of a jth user in the abnormality index cluster i; /(I)User health abnormality index indicating the jth user in abnormality index cluster i,/>Representing the average characteristic value of the characteristics o of all users in the abnormal index cluster i; /(I)And (5) representing the user health abnormality index mean value of all the users in the abnormality index cluster i.
It should be noted that, when the obtained confusion factor of the feature o in the abnormality index cluster i is larger, the larger the difference between the ratio of the feature value of the feature o corresponding to each user to the health abnormality index of the corresponding user in the abnormality index cluster i and the ratio of the average feature value of the feature o of all users in the abnormality index cluster i to the average value of the health indexes of the users of all users is larger, the larger the confusion degree of the feature o in the abnormality index cluster i is, that is, the larger the confusion index of the obtained abnormality index cluster i to the feature o is.
And meanwhile, acquiring the overall correlation between the feature o and the user health abnormality index according to different abnormality index clustering clusters.
Wherein,Representing the overall correlation between the feature o and the user health anomaly index, norm () represents the normalization function, K represents the number of clusters of anomaly indices,/>And the correlation between the characteristic o and the user health abnormality index in the abnormality index cluster i is represented.
It should be noted that, when the obtained confusion index of the feature o is larger in each abnormality index cluster obtained according to the user health abnormality index, the overall correlation between the feature o and the user health abnormality index is weaker.
Since the analysis is performed only as a whole to obtain the overall correlation between the feature and the user health abnormality index according to the above method, it is not considered that if the correlation between the features is strong in a certain user health abnormality index category, the feature information of the feature is ignored, and thus the feature is filtered out when the feature extraction is finally performed, and the result of abnormality monitoring according to the obtained feature is inaccurate, further correction of the obtained feature and the user health abnormality index is required.
Because the chaotic indexes of the characteristic values of different characteristic data in the abnormal index clustering cluster can be obtained when the clustering is carried out according to the user health abnormal indexes, the correlation among the characteristic data of different user health abnormal indexes can be further analyzed, and the contribution degree of the characteristic data to the user health abnormal indexes can be better obtained and analyzed.
The correlation method between any two feature data in the same cluster is as follows:
wherein, Representing the correlation between features o and s within an anomaly index cluster i, exp () representing an exponential function based on a natural constant e,/>、/>Respectively representing the correlation between the characteristics o and s in the abnormal index cluster i and the user health abnormal index,/>Representing the number of elements within the anomaly index cluster i,/>、Characteristic values of characteristic o and characteristic s of the jth user in the abnormal index cluster i are respectively represented by/>、/>And respectively representing the average characteristic values of the characteristics o and the characteristics s of all the users in the abnormal index cluster i.
It should be noted that, when the difference of the correlation between the two feature parameters o and s in the abnormality index cluster i and the user health abnormality index is smaller, and the difference of the ratio of the feature values of the two feature parameters corresponding to the abnormality index cluster i and the ratio of the average value of the two feature parameters in the abnormality index cluster is smaller, the stronger the correlation between the two feature parameters in the abnormality index cluster i is illustrated.
The correlation between all the features in the abnormal index cluster i and the features of the features o is calculated, and then the importance of the features o in the abnormal index cluster is analyzed, and the corresponding calculation method of the importance of the features o is as follows:
wherein, Representing the feature importance of the features o within the cluster i of abnormality indexes, L representing the feature data category,Representing the correlation between features o and features s within an abnormality index cluster i,/>And the correlation between the characteristic o and the user health abnormality index in the abnormality index cluster i is represented.
It should be noted that, when the correlation between the feature o and other features in the abnormality index cluster i is smaller, and the confusion index of the feature o in the abnormality index cluster i is larger, the importance of the feature o is smaller.
By using the method, the importance of each feature in each abnormal index cluster can be obtained, the numerical values of each feature under all abnormal index clusters are drawn into a feature curve graph, wherein the abscissa is each abnormal index cluster, the ordinate represents the feature importance of each feature in the corresponding abnormal index cluster, and the data points (r, k) in the feature curve graph represent the feature importance of the feature r in the abnormal index cluster k.
Analyzing the characteristic curve graph of each characteristic, analyzing the variation trend of the characteristic o along with the variation of the user health abnormality index, further obtaining the characteristic degree of the characteristic o by comparing the characteristic of the characteristic o with the characteristic curve graph of other characteristics along with the user health index, and finally analyzing according to the obtained characteristic degree to extract the characteristic parameter. The method for calculating the characteristic correction factor of the current characteristic according to the characteristic curve graph of the other characteristics and the characteristic curve graph analysis of the current characteristic is as follows:
wherein, Characteristic correction factor representing characteristic o-Represents a normalization function, L represents a feature data category,/>After the feature graph representing feature o and feature s is fitted, the similarity of the two resulting fitted curves is calculated using a shape context algorithm. The shape context algorithm is a well-known technique, and the description of this embodiment is omitted.
It should be noted that, when the similarity between the fitted curve of the feature o corresponding to the feature curve graph and the fitted curve of the other feature corresponding to the feature curve graph is smaller, the current feature o corresponding to the feature correction factor is larger.
The significance calculation method of the corresponding feature o is as follows:
wherein, Representing the significance of feature o,/>Representing the overall correlation between feature o and user health anomaly index,/>Representing feature importance mean value of feature o in all abnormal index clusters,/>The feature correction factor representing the feature o.
It should be noted that, the stronger the overall correlation between the feature o and the user health abnormality index is, the stronger the feature importance average value of the feature o in all abnormality index clusters is, and the stronger the feature correction factor of the feature o is, the greater the feature importance of the current feature o is, namelyThe larger the feature data is, the more consistent the key feature data in the user's health data.
And step S003, performing anomaly detection on the feature items based on the saliency screening by adopting an isolated forest algorithm to obtain evaluation of user health anomalies.
The saliency of each feature can be obtained through the steps, the saliency of each feature is divided by using an Ojin threshold method, and the feature with the saliency larger than the Ojin threshold is used as a key feature item. The method of threshold value of the body fluid is a well-known technique, and this embodiment will not be described in detail.
According to the method, feature extraction is carried out on each key feature item of the obtained user, the optimal parameters are selected, the abnormal score of the user at each moment is obtained by using an isolated forest algorithm, an index construction flow chart of the abnormal score of the user health is shown in fig. 2, health early warning is carried out on the user with the abnormal score larger than the preset threshold, and abnormal assessment on the health of the user is completed. The preset threshold is set by the practitioner according to the actual situation, the value of the embodiment is 0.7, the isolated forest algorithm is a known technology, and the embodiment is not repeated.
In summary, according to the embodiment of the invention, by analyzing the collected multidimensional data, the chaotic index of the characteristic item in the same abnormal index cluster where the user health abnormal index is located is calculated, and the correlation between the characteristic and the user health abnormal index is reflected based on the chaotic index, so that the chaotic index is used for evaluating the health regularity relation between the characteristic item and the user health data; meanwhile, the correlation among different features in the same user health anomaly cluster is calculated, and then the feature importance of the features is obtained by combining the correlation between the feature data and the user health anomaly indexes, and the importance degree of the features is analyzed from the whole and internal angles, so that the risk that important feature information is ignored is avoided, and the analysis is more comprehensive;
Further, according to trend conditions that the characteristics of different characteristic items change along with abnormal indexes of the health of the user, the trend similarity of any two characteristics is mined, and characteristic correction factors of the characteristics are constructed, so that the saliency of the characteristics is finally obtained, key characteristic items are extracted according to the saliency of characteristic data, and then the data of each key characteristic item are used for representing the characteristic degree of the health of the user, so that screening is carried out according to the obtained characteristic values, and the number of the characteristic data is reduced; and obtaining the abnormal score of the user health by using an isolated forest algorithm, and completing the monitoring of the abnormal user health. According to the embodiment of the invention, the key characteristic items are selected by reducing the dimensionality of the acquired data, so that the reliability of the abnormal value obtained by the isolated forest algorithm is greatly improved, and the accuracy of monitoring the health data of the user is greatly improved.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and the same or similar parts of each embodiment are referred to each other, and each embodiment mainly describes differences from other embodiments.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; the technical solutions described in the foregoing embodiments are modified or some of the technical features are replaced equivalently, so that the essence of the corresponding technical solutions does not deviate from the scope of the technical solutions of the embodiments of the present application, and all the technical solutions are included in the protection scope of the present application.
Claims (3)
1. The intelligent ring abnormal health data early warning method based on machine learning is characterized by comprising the following steps of:
Collecting user health data, including various characteristic values and artificially marked user health abnormality indexes;
Acquiring each characteristic cluster of each characteristic in each abnormal index cluster according to each user health abnormal index and the characteristic value; constructing a chaotic factor of each feature in the abnormal index cluster according to the feature cluster of each feature in the abnormal index cluster; constructing the correlation between each feature in the abnormality index cluster and the user health abnormality index according to the data difference between each feature and the integral feature in the abnormality index cluster and the chaotic factor; acquiring the overall correlation between each feature and the user health abnormality index according to the correlation between each feature and the user health abnormality index in different abnormality index cluster;
constructing the correlation between any two features in the abnormal index cluster according to the correlation between each feature in the abnormal index cluster and the user health abnormal index and the difference of the feature values; constructing the feature importance of each feature in the abnormal index cluster according to the correlation between any two features in the abnormal index cluster and the correlation between each feature and the user health abnormal index; constructing a feature correction factor of each feature based on the trend distribution similarity of feature importance among the features; constructing the significance of each feature according to the overall correlation between each feature and the user health abnormality index, the feature importance and the feature correction factor;
Acquiring key feature items based on significance, and evaluating the health abnormal condition of the user by adopting an abnormality detection algorithm;
The construction of the chaotic factors of each feature in the abnormality index cluster according to the feature cluster of each feature in the abnormality index cluster comprises the following steps:
for each characteristic cluster of each characteristic in the abnormal index cluster, acquiring the absolute value of the difference value of the element number between any two characteristic clusters and the absolute value of the difference value of the average characteristic value of all elements;
Acquiring the number of feature clusters of each feature in the abnormality index cluster; calculating the sum value of products of absolute values of differences of element numbers and absolute values of differences of average feature values of all elements between any two feature clusters in each feature in the abnormal index cluster, and taking the products of the first sum value and the feature cluster numbers as chaotic factors of each feature in the abnormal index cluster;
the step of obtaining the overall correlation between each feature and the user health abnormality index according to the correlation between each feature and the user health abnormality index in different abnormality index cluster comprises the following steps:
Calculating a normalized value of a sum value of the correlations between each feature and the user health abnormality index of all abnormality index clusters, and taking a difference value between a number 1 and the normalized value as an overall correlation between each feature and the user health abnormality index;
the method comprises the steps of constructing the correlation between any two characteristics in an abnormal index cluster according to the correlation between each characteristic in the abnormal index cluster and a user health abnormal index and the characteristic value difference, wherein the expression is as follows:
wherein, Representing the correlation between features o and features s within an abnormality index cluster i,/>Representing an exponential function based on natural constants,/>、/>Respectively representing the correlation between the characteristics o and s in the abnormal index cluster i and the user health abnormal index,/>Representing the number of elements within the anomaly index cluster i,/>、/>Characteristic values of characteristic o and characteristic s of the jth user in the abnormal index cluster i are respectively represented by/>、/>Respectively representing average characteristic values of the characteristics o and the characteristics s of all users in the abnormal index cluster i;
the construction of the feature importance of each feature in the abnormality index cluster according to the correlation between any two features in the abnormality index cluster and the correlation between each feature and the user health abnormality index comprises the following steps:
calculating the sum value of the correlation between each feature and all the remaining features in the abnormal index cluster as a second sum value for each feature in the abnormal index cluster; taking the ratio of the correlation between each feature in the second sum value and the abnormality index cluster and the user health abnormality index as the feature importance of each feature in the abnormality index cluster;
the feature correction factor of each feature is constructed based on the trend distribution similarity condition of feature importance among the features, and the method comprises the following steps:
Forming a feature graph for each feature based on the feature importance of each feature within the respective anomaly index clusters;
Calculating the sum value of the similarity between the curve fitted by the characteristic curve graph of each characteristic and all other characteristics, recording the sum value as a third sum value, and taking the difference value between the number 1 and the normalized value of the third sum value as a characteristic correction factor of each characteristic; the similarity is calculated by a shape context algorithm;
The constructing the significance of each feature according to the overall correlation between each feature and the user health abnormality index, the feature importance and the feature correction factor comprises the following steps:
Calculating the sum of the feature correction factor and the number 1 of each feature to be recorded as a fourth sum; acquiring feature importance average values of each feature in all abnormal index cluster; taking the fourth sum, the feature importance mean, and the product of the overall correlation between each feature and the user health anomaly index as the significance of each feature;
the obtaining of the key feature items based on the salience adopts an abnormality detection algorithm to evaluate the health abnormality of the user, and comprises the following steps:
The saliency of all the features is used as input of an Ojin threshold method, an Ojin threshold is obtained, and the features corresponding to the saliency larger than the Ojin threshold are used as key feature items;
The feature data of all key feature items of each user are subjected to an isolated forest algorithm, and the abnormal score of each user is obtained; and carrying out health early warning on the users with the anomaly scores larger than the preset threshold value.
2. The machine learning-based intelligent ring abnormal health data early warning method according to claim 1, wherein the obtaining each feature cluster of each feature in each abnormal index cluster according to each user health abnormal index and a feature value comprises:
obtaining clustering clusters of the user health abnormality indexes of all users by adopting a clustering algorithm, and marking the clustering clusters as the clustering clusters of the abnormality indexes;
Clustering the characteristic values of each characteristic in each abnormal index cluster in all users to obtain each cluster, and marking the cluster as each characteristic cluster of each characteristic in each abnormal index cluster.
3. The machine learning based intelligent ring abnormal health data pre-warning method according to claim 1, wherein the constructing the correlation between each feature in the cluster of abnormality indexes and the user health abnormality index based on the data difference between each feature in the cluster of abnormality indexes and the overall feature and the clutter factor comprises:
For each user in the abnormal index cluster, calculating the ratio of the characteristic value of each user in each characteristic to the user health abnormal index of each user; calculating the ratio of the average characteristic value of all the users in each characteristic to the average value of the user health abnormality indexes of all the users;
Obtaining a chaotic factor of each feature in the abnormal index cluster; multiplying the sum of absolute values of differences between two ratios of all users in the abnormality index cluster by the chaotic factor to obtain the correlation between each feature in the abnormality index cluster and the health abnormality index of the user.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410275598.9A CN117877736B (en) | 2024-03-12 | 2024-03-12 | Intelligent ring abnormal health data early warning method based on machine learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410275598.9A CN117877736B (en) | 2024-03-12 | 2024-03-12 | Intelligent ring abnormal health data early warning method based on machine learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117877736A CN117877736A (en) | 2024-04-12 |
| CN117877736B true CN117877736B (en) | 2024-05-24 |
Family
ID=90595224
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410275598.9A Active CN117877736B (en) | 2024-03-12 | 2024-03-12 | Intelligent ring abnormal health data early warning method based on machine learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117877736B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020038353A1 (en) * | 2018-08-21 | 2020-02-27 | 瀚思安信（北京）软件技术有限公司 | Abnormal behavior detection method and system |
| CN113657549A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Medical data auditing method, device, equipment and storage medium |
| CN116484307A (en) * | 2023-06-21 | 2023-07-25 | 深圳市魔样科技有限公司 | Cloud computing-based intelligent ring remote control method |
| CN116628617A (en) * | 2023-07-25 | 2023-08-22 | 值数科技(北京)有限公司 | Method for realizing miniature strain monitoring based on nanocomposite |
| CN116735807A (en) * | 2023-08-09 | 2023-09-12 | 山东优控智能技术有限公司 | Air quality detection and evaluation method based on multi-sensor data |
| CN116933046A (en) * | 2023-09-19 | 2023-10-24 | 山东大学 | Deep learning-based multi-mode health management scheme generation method and system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10373065B2 (en) * | 2013-03-08 | 2019-08-06 | Oracle International Corporation | Generating database cluster health alerts using machine learning |
| US20230419402A1 (en) * | 2022-06-23 | 2023-12-28 | The Toronto-Dominion Bank | Systems and methods of optimizing machine learning models for automated anomaly detection |
-
2024
- 2024-03-12 CN CN202410275598.9A patent/CN117877736B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2020038353A1 (en) * | 2018-08-21 | 2020-02-27 | 瀚思安信（北京）软件技术有限公司 | Abnormal behavior detection method and system |
| CN113657549A (en) * | 2021-08-31 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Medical data auditing method, device, equipment and storage medium |
| CN116484307A (en) * | 2023-06-21 | 2023-07-25 | 深圳市魔样科技有限公司 | Cloud computing-based intelligent ring remote control method |
| CN116628617A (en) * | 2023-07-25 | 2023-08-22 | 值数科技(北京)有限公司 | Method for realizing miniature strain monitoring based on nanocomposite |
| CN116735807A (en) * | 2023-08-09 | 2023-09-12 | 山东优控智能技术有限公司 | Air quality detection and evaluation method based on multi-sensor data |
| CN116933046A (en) * | 2023-09-19 | 2023-10-24 | 山东大学 | Deep learning-based multi-mode health management scheme generation method and system |
Non-Patent Citations (2)
| Title |
|---|
| Yilong Shi ; Yirong Liu ; Xiang Gao.Study of Wind Turbine Fault Diagnosis and Early Warning Based on SCADA Data.IEEE Access.2021,124600-124615. * |
| 基于可穿戴设备和服务机器人的血糖预测方法;刘赛赛;中国硕士论文库;20221215;1-67 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117877736A (en) | 2024-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116705337B (en) | Health data acquisition and intelligent analysis method | |
| Acharya et al. | Automated identification of normal and diabetes heart rate signals using nonlinear measures | |
| CN109009017B (en) | Intelligent health monitoring system and data processing method thereof | |
| CN109864736A (en) | Processing method, device, terminal device and the medium of electrocardiosignal | |
| CN110289061B (en) | Time sequence prediction method for traumatic hemorrhagic shock injury | |
| Cömert et al. | Evaluation of fetal distress diagnosis during delivery stages based on linear and nonlinear features of fetal heart rate for neural network community | |
| CN112641451B (en) | Multi-scale residual error network sleep staging method and system based on single-channel electroencephalogram signal | |
| CN112043252B (en) | Emotion recognition system and method based on respiratory component in pulse signal | |
| CN113499035B (en) | Pain identification system based on confidence interval fusion threshold criterion | |
| Swapna et al. | Automated detection of diabetes using higher order spectral features extracted from heart rate signals | |
| Baghizadeh et al. | A new emotion detection algorithm using extracted features of the different time-series generated from ST intervals Poincaré map | |
| CN111370124A (en) | Health analysis system and method based on facial recognition and big data | |
| Gao et al. | Real time ECG characteristic point detection with randomly selected signal pair difference (RSSPD) feature and random forest classifier | |
| WO2023097780A1 (en) | Classification method and device for classifying patient‑ventilator asynchrony phenomenon in mechanical ventilation process | |
| Hidayat et al. | Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification | |
| CN116864062B (en) | Health physical examination report data analysis management system based on Internet | |
| CN113903466A (en) | Data processing device and system for auxiliary evaluation of risk degree of cardiovascular diseases of population and application of data processing device and system | |
| CN117877736B (en) | Intelligent ring abnormal health data early warning method based on machine learning | |
| Yeh | An Analysis of ECG for Determining Heartbeat Case by Using the Principal Component Analysis and Fuzzy Logic. | |
| CN114464319A (en) | AMS susceptibility assessment system based on slow feature analysis and deep neural network | |
| CN113349746A (en) | Vital sign monitoring alarm system | |
| Li et al. | A dirichlet process mixture model for autonomous sleep apnea detection using oxygen saturation data | |
| CN113111918A (en) | Cerebral apoplexy risk screening method based on abnormal sample detection and multi-dimensional information output | |
| CN112507959A (en) | Method for establishing emotion perception model based on individual face analysis in video | |
| Bhopte et al. | Hybrid Deep learning CNN-LSTM Model for Diabetes Prediction |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: 518000 1st, 2nd, 4th, 5th and 6th floors of Qiufen (10A) Building, Taihua wutong Industrial Park, Sanwei Community, Hangcheng Street, Bao'an District, Shenzhen, Guangdong Applicant after: Shenzhen Moyang Technology Co.,Ltd. Address before: 518000 1st, 2nd, 4th, 5th and 6th floors of Qiufen (10A) Building, Taihua wutong Industrial Park, Sanwei Community, Hangcheng Street, Bao'an District, Shenzhen, Guangdong Applicant before: SHENZHEN MOYOUNG TECHNOLOGY CO.,LTD. Country or region before: China |
|
| GR01 | Patent grant |